Nowadays, a plurality of computing resources and data-acquisition technologies are employed for collecting data. Data are pieces of information that represents the quantitative and qualitative attributes of one or more objects. Examples of data may include, but are not limited to a number, a letter, a word, a sentence, a symbol, a graph, a picture, an image and a character. An object may represent an idea, a variable, a product, a subject, a concept, a physical phenomenon, a psychic phenomenon, an entity and so on. For example, a physical phenomenon like weather is an object. Similarly, a product like a Stock Keeping Unit (SKU) is an object. In another example, a concept like number of people who like Pink Floyd is an object. The one or more objects are usually stored in a memory. Correlations between the one or more objects are usually identified for sorting the data, for identifying relationship between the one or more objects, for predicting or anticipating a value of the one or more objects in future, past and present and for determining variance of a dependent object from an independent object.
However, with the exponential growth in volume and type of data associated with the one or more objects it is becoming increasingly difficult to analyze, sort and reduce the data and to identify a relation, a correlation, a variance and a covariance associated with the one or more objects. Subject matter experts who have knowledge and information associated with the one or more objects are usually employed to analyze the one or more objects. These subject matter experts identify the relation, the correlation, the variance and the covariance associated with the one or more objects. Moreover, these subject matter experts analyze the one or more objects to identify relationship patterns and missing data, compute statistics, filter objects matching a certain criteria and so on. For example, a meteorologist may analyze weather data for the last five years of a city to identify patterns and correlation between weather parameters like temperature, humidity, soil temperature and wind direction and so on. In another example, an expert having knowledge about weather and stock markets may identify patterns and detect correlation between the weather data and the stock market data. However, with an increase in the number of input, input variables, independent variables, dependent variables and exogenous variables associated with the one or more objects, the subject matter experts find it difficult to analyze the one or more objects.
Some of the existing technologies employ various tools for detecting correlation associated with the one or more objects. Examples of such tools may include, but are not limited to Matlab©, Mathematica©, Excel©, Statistical Analysis System (SAS©), Scientific Publishing Services (SPS©), S-Plus© and Forecast Expert Toolkit©. These tools may need a human or a subject matter expert to analyze the one or more objects.
Therefore, there is a need for a method and a system to analyze the one or more objects for identifying correlations associated with the one or more objects in an efficient manner.